Szl0123's starred repositories
optimal-charging-of-li-ion-batteries
Implement model predictive control on a physics-based battery model for minimizing charging time while maximizing lifetime.
transformer-multi-step-time-series-prediction
Battery temperature prediction
AI-Based-Prediction-Algorithm-For-The-Battery-Life
An LSTM based neural network to predict RUL of Li-ion battery.
Ultra-early-performance-prediction
Data and code for the paper "Ultra-early prediction of lithium-ion battery performance using mechanism and data-driven fusion model"
EWT-Capacity-Estimation
Unofficial Reproduction: Capacity estimation of lithium-ion batteries based on adaptive empirical wavelet transform and long short-term memory neural network(Journal of Energy Storage 2023)
cycle-consistency-transformer
Unofficial reproduction of: A transferable lithium-ion battery remaining useful life prediction method from cycle-consistency of degradation trend(2022)
battery_prediction_model_reproduction
復現學長的鋰電池壽命預測模型
Battery_Cycle_Life_Prediction_Pytorch
Life cycle prediction model for batteries
lightweight-models
pytorch implement
LightWeightModel
LightWeightModel_Spectral_Reconstruction
awesome-AutoML-and-Lightweight-Models
A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.
battery_life_prediction
Implementation for data driven prediction of battery cycle life before capacity degradation
CNN-ASTLSTM
Code for paper "An end-to-end neural network framework for SOH estimation and RUL prediction of lithium battery"
Energitic-project-1
This study pioneers E-LSTM and CNN-LSTM deep learning models for precise Lithium-Ion Battery State of Health (SOH) prediction. Using MIT's battery dataset, our interpretable models, enhanced by Shapley Additive exPlanations and pattern mining, offer promising results.
long-live-the-battery
RNN-flavored Ensembling to Predict Remaining Useful Life of Lithium-ion Batteries
battery_state_prediction
Battery state of charge prediction based on machine learning algorithm for competition
Predictive-Maintenance
Leveraging Deep Learning Solutions for Predictive Maintenance of Batteries in Industrial Datasets
transferlearning
Everything about Transfer Learning and Domain Adaptation--迁移学习
LSTM-Neural-Network-for-Time-Series-Prediction
Battery data processing.
Enhanched-Search-Mechanism-for-Harris-Hawks-Optimizer-using-Honey-Badger-Algorithm
This project creates a hybrid algorithm named Honey Badger-Harris Hawk Optimizer which is capable of solving optimization tasks.
Nonlinear-based-Chaotic-Harris-Hawks-Optimization_Internet-of-Vehicles_Application
NCHHO uses chaotic and nonlinear control parameters to improve HHO’s optimization performance. The main goal of using the chaotic maps in the proposed method is to improve the exploratory behavior of HHO. In addition, this paper introduces a nonlinear control parameter to adjust HHO’s exploratory and exploitative behaviours. The proposed NCHHO algorithm shows an improved performance using a variety of chaotic maps that were implemented to identify the most effective one, and tested on several well-known benchmark functions. Also, this work considers solving an Internet of Vehicles (IoV) optimization problem that showcases the applicability of NCHHO in solving large-scale, real-world problems.
LSTM_encoder_decoder
Build a LSTM encoder-decoder using PyTorch to make sequence-to-sequence prediction for time series data
bat-age-data-scripts
Python example scripts for processing the battery aging data published in [insert description and link here]